Google BigQuery vs Hibernate

Overview

Google BigQuery

Stacks1.8K

Followers1.5K

Votes152

Hibernate

Stacks1.8K

Followers1.2K

Votes34

GitHub Stars0

Forks0

Google BigQuery vs Hibernate: What are the differences?

Introduction

This markdown code provides a comparison between Google BigQuery and Hibernate, highlighting the key differences between the two.

Scalability and Performance: Google BigQuery is a cloud-based data warehouse that is highly scalable and can handle large volumes of data with ease. It is built on Google's infrastructure, which allows for fast and efficient data processing. On the other hand, Hibernate is an Object-Relational Mapping (ORM) framework that provides a way to map Java objects to relational database tables. It focuses on providing a convenient and efficient way to perform database operations on a smaller scale compared to BigQuery.
Data Storage and Querying: Google BigQuery allows users to store large amounts of data in a structured, columnar format. It provides a rich SQL-like querying language that allows for advanced analytics and aggregations on the data. Hibernate, on the other hand, relies on traditional relational databases for data storage and querying. It provides a powerful object-oriented querying language called Hibernate Query Language (HQL), which is similar to SQL but focuses on manipulating Java objects rather than raw data.
Cost and Pricing Model: Google BigQuery follows a pay-as-you-go pricing model, where users are charged based on the amount of data stored and the queries performed. It offers different pricing tiers based on usage, allowing users to choose the most cost-effective option for their needs. Hibernate, on the other hand, is an open-source framework and does not have any direct cost associated with it. However, users may incur costs related to database licensing and hosting if they choose to use Hibernate with a commercial database.
Ease of Use and Learning Curve: Google BigQuery is a cloud-based service that abstracts the underlying infrastructure, making it easy to set up and use. It provides a user-friendly web interface and supports multiple programming languages for integration. Hibernate, on the other hand, requires some initial setup and configuration. It has a steeper learning curve compared to BigQuery, especially for developers new to ORM frameworks. However, once set up, Hibernate provides a convenient way to perform database operations using familiar Java syntax.
Availability and Deployment: Google BigQuery is a fully managed service provided by Google Cloud Platform. It ensures high availability and reliability, with automatic backups and replication of data across multiple data centers. Hibernate, on the other hand, can be used with various databases, both local and cloud-based. The availability and deployment of Hibernate depend on the chosen database and hosting provider.

In Summary, Google BigQuery is a cloud-based, highly scalable data warehouse with a SQL-like querying language, while Hibernate is an ORM framework for Java applications that focuses on mapping objects to relational databases. The key differences include scalability and performance, data storage and querying, cost and pricing model, ease of use and learning curve, and availability and deployment.

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Advice on Google BigQuery, Hibernate

Julien

CTO at Hawk

Sep 19, 2020

Decided

Cloud Data-warehouse is the centerpiece of modern Data platform. The choice of the most suitable solution is therefore fundamental.

Our benchmark was conducted over BigQuery and Snowflake. These solutions seem to match our goals but they have very different approaches.

BigQuery is notably the only 100% serverless cloud data-warehouse, which requires absolutely NO maintenance: no re-clustering, no compression, no index optimization, no storage management, no performance management. Snowflake requires to set up (paid) reclustering processes, to manage the performance allocated to each profile, etc. We can also mention Redshift, which we have eliminated because this technology requires even more ops operation.

BigQuery can therefore be set up with almost zero cost of human resources. Its on-demand pricing is particularly adapted to small workloads. 0 cost when the solution is not used, only pay for the query you're running. But quickly the use of slots (with monthly or per-minute commitment) will drastically reduce the cost of use. We've reduced by 10 the cost of our nightly batches by using flex slots.

Finally, a major advantage of BigQuery is its almost perfect integration with Google Cloud Platform services: Cloud functions, Dataflow, Data Studio, etc.

BigQuery is still evolving very quickly. The next milestone, BigQuery Omni, will allow to run queries over data stored in an external Cloud platform (Amazon S3 for example). It will be a major breakthrough in the history of cloud data-warehouses. Omni will compensate a weakness of BigQuery: transferring data in near real time from S3 to BQ is not easy today. It was even simpler to implement via Snowflake's Snowpipe solution.

We also plan to use the Machine Learning features built into BigQuery to accelerate our deployment of Data-Science-based projects. An opportunity only offered by the BigQuery solution

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Detailed Comparison

Google BigQuery	Hibernate
Run super-fast, SQL-like queries against terabytes of data in seconds, using the processing power of Google's infrastructure. Load data with ease. Bulk load your data using Google Cloud Storage or stream it in. Easy access. Access BigQuery by using a browser tool, a command-line tool, or by making calls to the BigQuery REST API with client libraries such as Java, PHP or Python.	Hibernate is a suite of open source projects around domain models. The flagship project is Hibernate ORM, the Object Relational Mapper.
All behind the scenes- Your queries can execute asynchronously in the background, and can be polled for status.;Import data with ease- Bulk load your data using Google Cloud Storage or stream it in bursts of up to 1,000 rows per second.;Affordable big data- The first Terabyte of data processed each month is free.;The right interface- Separate interfaces for administration and developers will make sure that you have access to the tools you need.	-
Statistics
GitHub Stars -	GitHub Stars 0
GitHub Forks -	GitHub Forks 0
Stacks 1.8K	Stacks 1.8K
Followers 1.5K	Followers 1.2K
Votes 152	Votes 34
Pros & Cons
Pros 28 High Performance 25 Easy to use 22 Fully managed service 19 Cheap Pricing 16 Process hundreds of GB in seconds Cons 1 You can't unit test changes in BQ data 0 Sdas	Pros 22 Easy ORM 8 Easy transaction definition 3 Is integrated with spring jpa 1 Open Source Cons 3 Can't control proxy associations when entity graph used
Integrations
Xplenty Fluentd Looker Chartio Treasure Data	Java

What are some alternatives to Google BigQuery, Hibernate?

Sequelize

Sequelize is a promise-based ORM for Node.js and io.js. It supports the dialects PostgreSQL, MySQL, MariaDB, SQLite and MSSQL and features solid transaction support, relations, read replication and more.

Amazon Redshift

It is optimized for data sets ranging from a few hundred gigabytes to a petabyte or more and costs less than $1,000 per terabyte per year, a tenth the cost of most traditional data warehousing solutions.

Qubole

Qubole is a cloud based service that makes big data easy for analysts and data engineers.

Prisma

Prisma is an open-source database toolkit. It replaces traditional ORMs and makes database access easy with an auto-generated query builder for TypeScript & Node.js.

Amazon EMR

It is used in a variety of applications, including log analysis, data warehousing, machine learning, financial analysis, scientific simulation, and bioinformatics.

Doctrine 2

Doctrine 2 sits on top of a powerful database abstraction layer (DBAL). One of its key features is the option to write database queries in a proprietary object oriented SQL dialect called Doctrine Query Language (DQL), inspired by Hibernates HQL.

Altiscale

we run Apache Hadoop for you. We not only deploy Hadoop, we monitor, manage, fix, and update it for you. Then we take it a step further: We monitor your jobs, notify you when something’s wrong with them, and can help with tuning.

Snowflake

Snowflake eliminates the administration and management demands of traditional data warehouses and big data platforms. Snowflake is a true data warehouse as a service running on Amazon Web Services (AWS)—no infrastructure to manage and no knobs to turn.

MikroORM

TypeScript ORM for Node.js based on Data Mapper, Unit of Work and Identity Map patterns. Supports MongoDB, MySQL, MariaDB, PostgreSQL and SQLite databases.

Entity Framework

It is an object-relational mapper that enables .NET developers to work with relational data using domain-specific objects. It eliminates the need for most of the data-access code that developers usually need to write.

Related Comparisons

Google BigQuery vs Hibernate: What are the differences?

Introduction

This markdown code provides a comparison between Google BigQuery and Hibernate, highlighting the key differences between the two.

Scalability and Performance: Google BigQuery is a cloud-based data warehouse that is highly scalable and can handle large volumes of data with ease. It is built on Google's infrastructure, which allows for fast and efficient data processing. On the other hand, Hibernate is an Object-Relational Mapping (ORM) framework that provides a way to map Java objects to relational database tables. It focuses on providing a convenient and efficient way to perform database operations on a smaller scale compared to BigQuery.
Data Storage and Querying: Google BigQuery allows users to store large amounts of data in a structured, columnar format. It provides a rich SQL-like querying language that allows for advanced analytics and aggregations on the data. Hibernate, on the other hand, relies on traditional relational databases for data storage and querying. It provides a powerful object-oriented querying language called Hibernate Query Language (HQL), which is similar to SQL but focuses on manipulating Java objects rather than raw data.
Cost and Pricing Model: Google BigQuery follows a pay-as-you-go pricing model, where users are charged based on the amount of data stored and the queries performed. It offers different pricing tiers based on usage, allowing users to choose the most cost-effective option for their needs. Hibernate, on the other hand, is an open-source framework and does not have any direct cost associated with it. However, users may incur costs related to database licensing and hosting if they choose to use Hibernate with a commercial database.
Ease of Use and Learning Curve: Google BigQuery is a cloud-based service that abstracts the underlying infrastructure, making it easy to set up and use. It provides a user-friendly web interface and supports multiple programming languages for integration. Hibernate, on the other hand, requires some initial setup and configuration. It has a steeper learning curve compared to BigQuery, especially for developers new to ORM frameworks. However, once set up, Hibernate provides a convenient way to perform database operations using familiar Java syntax.
Availability and Deployment: Google BigQuery is a fully managed service provided by Google Cloud Platform. It ensures high availability and reliability, with automatic backups and replication of data across multiple data centers. Hibernate, on the other hand, can be used with various databases, both local and cloud-based. The availability and deployment of Hibernate depend on the chosen database and hosting provider.

Google BigQuery vs Hibernate

Overview

Google BigQuery vs Hibernate: What are the differences?